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Data Science With Python Online Training

(4.5) 17968 Ratings
  • Enroll the Best Data Science With Python Online Course to Analyze, Visualize, and Predict Data.
  • Learn Python, NumPy, Pandas, Matplotlib, Scikit-learn, Stats, and Machine Learning Techniques.
  • Earn a Data Science Certification with Job-Oriented Coaching and Placement Guidance.
  • Attend Online Classes with Flexible Timing: Weekdays, Weekends, or Fast-Track Schedules.
  • Work on Capstone Projects and Real-World Problems Guided by Professional Data Scientists.
  • Benefit from Mock Interviews, Resume Reviews, and Career Support for Data Science Jobs.

Course Duration

50+ Hrs

Live Project

3 Project

Certification Pass

Guaranteed

Training Format

Live Online (Expert Trainers)
WatchLive Classes
Course fee at
₹16000

₹21000

12057+

Professionals Trained

9+

Batches every month

3075+

Placed Students

265+

Corporate Served

What You'll Learn

Learn Python fundamentals and libraries like Pandas, NumPy, and Matplotlib for data analysis in Data Science With Python Online Training .

Master data cleaning techniques to prepare raw data for analysis and machine learning models.

Data Science With Python Online Course Understand data visualization concepts to create insightful charts and graphs using Python.

Gain expertise in a machine learning algorithms such as regression, classification, and clustering.

Explore real-world data science projects to apply knowledge in solving industry-specific problems.

Build and deploy machine learning model using Python for predictive analysis and forecasting.

Comprehensive Overview of Data Science With Python Course

Our Data Science With Python Online Training is designed to equip learner with essential skills to excel in data science using Python. Through our Data Science With Python Online Course, you will gain hands-on experience with libraries like Pandas, NumPy, Matplotlib, and Scikit-learn, mastering techniques in data analysis, machine learning & data visualization. This course also includes real-world projects that provide valuable practical experience, helping you solve complex data-related problem By the end of the program you will be eligible for the Data Science With Python Certification Course. Furthermore our Data Science With Python Placement assistance ensures that you are connected with top industry recruiters, helping you launch a successful career in rapidly growing field of data science Whether you're a beginner or looking to enhance your skills our training prepares you for the evolving demands of the data science industry.

Additional Info

Future Developments in Data Science With Python Course

  • Automated Machine Learning (AutoML): Automated machine learning tools will continue to gain traction in data science, enabling professionals to build models without extensive manual coding. Python libraries like Auto-sklearn and TPOT are already simplifying model selection hyperparameter tuning and data preprocessing. This trend will democratize machine learning, making it accessible to non-experts while enhancing efficiency for seasoned data scientist. As AutoML evolves Python’s role in driving these tools will be crucial for industry adoption.
  • Artificial Intelligence (AI) and Machine Learning Integration: AI and machine learning will be become more integrated into Python based data science workflows, making complex models more accessible and powerful. Python's libraries such as TensorFlow, Keras and PyTorch will continue to be at the forefront of deep learning advancements. The future will see a surge in AI powered tools for data analysis, predictive modeling, and automation with Python as the core language facilitating seamless integration between AI and data science.
  • Big Data Analytics: With the exponential growth of data, Python’s capability to handle big data will evolve. Libraries like PySpark and Dask enable distributed data processing which will become essential for data scientists handling enormous datasets The future of data science With Python will focus on scalable systems that can process, analyze and visualize data at unprecedented volumes thus driving business intelligence and real-time analytics.
  • Natural Language Processing (NLP) Expansion: Natural Language Processing will continue to grow in importance, enabling machines to understand human language more effectively. Pythons NLTK and spaCy libraries will evolve, providing more powerful tools for text analytic. Future trends will see the rise of chatbots, sentiment analysis and automated content generation all driven by Python-based NLP models This trend will open new avenues for data scientists to create innovative applications for social media healthcare and more.
  • Data Visualization and Interactive Dashboards: The demand for a advanced data visualization tools will skyrocket, with Python's Plotly, Seaborn and Dash continuing to lead the charge. Data scientists will increasingly focus on creating interactive dashboards and visualizations that offer more insightful and dynamic data presentations. The future will see a fusion of machine learning models with real-time interactive visualizations, providing a more intuitive experience for decision-makers in businesses and organization.
  • Edge Computing for Real-Time Analytics: Edge computing will play a critical role processing data at the source rather than sending it to centralized cloud servers. Data science With Python will leverage this technology, especially in IoT (Internet of Things) devices, to deliver real-time data processing and decision making. As edge computing evolves Python will be pivotal in creating efficient data pipelines and deploying machine learning models directly on edge devices.
  • Ethics and Bias in Machine Learning Models: As data science grows, there will be an increasing emphasis on the ethical consideration of machine learning algorithms. Python based libraries will evolve to address algorithmic bias, data privacy and fairness in decision making Future trends will include more robust frameworks for ensuring the transparency and accountability of AI models Data scientists will be trained to implement ethical standards in their models, using Python tools to mitigate biases and ensure equitable outcomes.
  • Quantum Computing in Data Science: The integration of quantum computing in data science will begin reshaping the way data scientists approach problem solving Python will become essential in developing algorithms that utilize quantum computing power accelerating data processing and model training times. While quantum computing is still in its infancy its future potential will bring transformative advancements to data science by solving complex problems far beyond the reach of traditional computing system.
  • Cloud-Based Data Science Platforms: Cloud computing will be remain an dominant of force in the future of data science offering scalable resources for analysis model training, and deployment. Python's integration with cloud platforms like AWS, Azure and Google Cloud will simplify the process of deploying and scaling data science models. Data scientists will increasingly use cloud-based tools for collaboration enabling faster iteration and model development. The future will likely see more cloud-based Python frameworks tailored to data science workflow.
  • Data Science Automation and Pipelines: Automation in data science will continue to be a key trend with Python driving the development of automated data pipelines and workflows. Tools like Apache Airflow and Luigi will be essential in automating tasks such as data extraction, transformation and loading. This will allow data scientist to focus on more complex problem solving while reducing manual errors and inefficiencies The future of data science will involve streamlined automation that supports large scale projects and enhances productivity across industries.

Building Tools and Techniques with Data Science With Python Course

  • Python: Python is the backbone of data science, offering simplicity and wide range of libraries for data manipulation, analysis and machine learning. Libraries like Pandas, NumPy and SciPy make Python the preferred language for data scientists, providing tools for data wrangling, numerical computations, and statistical analysis. Pythons versatility, ease of learning and strong community support make it an essential tool in every data scientists toolkit. The language integrates seamlessly with other technologies, enabling scalability and real time analytics. As the demand for data science grows Python remains central to training and real world applications.
  • Pandas: Pandas is a open source Python library designed for data manipulation and analysis It provides easy to use data structures like DataFrames which are ideal for handling & analyzing structured data. With Pandas data scientists can clean, filter, group and perform statistical operations efficiently This tool plays a crucial role in preprocessing data which is one of the most time consuming parts of the data science workflow. Whether working with time series, large datasets or handling missing data Pandas is an indispensable tool for data analysis.
  • NumPy: NumPy is a foundational package for scientific computing in Python, providing support for large multidimensional arrays and matrices With its vast collection of mathematical functions, NumPy allows data scientists to perform complex mathematical computations from linear algebra to Fourier analysis NumPys array based structure allows for fast, efficient operations on large datasets, making it ideal for data manipulation and preparation As a core dependency for libraries like Pandas and Scikit learn NumPy is essential for any data science project involving numerical computations or high performance computing tasks.
  • Matplotlib: Matplotlib is a powerful Python library used for creating static, animated and interactive visualizations. It allows data scientists to generate charts, graphs and plots to represent data in a visually accessible format. With Matplotlib you can create line charts, histograms, bar graphs, scatter plots and much more. The library integrates easily with other data science tools like Pandas and NumPy, enabling seamless data visualization workflows. It is a go to tool for presenting insights and making data driven decisions through compelling visual narrative.
  • Seaborn: Seaborn builds on Matplotlib and provides a high level interface for creating attractive and informative statistical graphics It simplifies the process of a generating complex visualization like heatmaps, violin plots and pair plots which are crucial for understanding relationships in datasets Seaborn works well with Pandas DataFrames, making it easy to plot and analyze data directly from them This tool also offers automatic color palettes and complex plot styling, allowing data scientists to focus on the data analysis rather than plot formatting. Seaborn is essential for anyone looking to create aesthetically pleasing and insightful data visualizations.
  • Scikit-learn: Scikit-learn is comprehensive machine learning library in Python that offer simple & efficient tools for data mining and data analysis It includes wide variety of ML algorithms for classification, regression, clustering & dimensionality reduction. With built-in functions for model selection cross-validation, and evaluation, Scikit-learn makes it easy for data scientists to build, test, and optimize machine learning models. It integrates smoothly with other Python libraries like NumPy and Pandas allowing seamless workflows for data preprocessing and modeling Scikit learn remains one of the most popular tools for a implementing machine learning algorithms.
  • TensorFlow: TensorFlow is open source machine learning framework developed by Google, used for building and deploying deep learning models. It allows data scientists to create neural networks for a tasks like image recognition, natural language processing and speech recognition TensorFlows flexibility and scalability make it suitable for both beginners & advanced users, offering both high level API for a ease use and low level API for more complex tasks With TensorFlow data scientists can leverage powerful hardware like GPUs and TPUs to train large models efficiently It is a core technology in the deep learning ecosystem, making it indispensable for modern data science projects.
  • Keras: Keras is high level neural networks API written in a Python that runs on top of TensorFlow. It is designed for fast prototyping & experimentation, providing a easy to use interface for building & training deep learning models. Keras simplifies the process of model creation by offering a simple API for defining, compiling, and training deep learning models It supports both convolutional and recurrent networks, making it versatile for wide range of application Keras is widely used for developing and deploying AI models due to its ease of use & powerful capabilities for handling complex deep learning tasks.
  • Jupyter Notebooks: Jupyter Notebooks are open source web application to create and share live code, equations, visualization and narrative text It is an essential tool are data scientist and machine learning practitioners to document their workflows and perform interactive data analysis. With Jupyter you can combine code execution, visualizations and markdown in a single document making it easier to present findings Its interactive nature allows quick iteration on ideas, making it a go to tool for learning, experimentation and collaboration in data science.
  • Apache Spark: Apache Spark is powerful open source distributed computing system that widely used for big data processing & analytics. Spark provides high-level APIs in Python, Java and Scala, allowing data scientists to process large datasets quickly and efficiently across multiple nodes. With its ability to handle both batch and real time data processing, Spark is ideal for large scale machine learning and data analysis tasks. It integrates well with Python libraries like Pandas and Scikit learn, enabling data scientists to scale their models and analyses to big data environments Spark is critical for organizations working with big data, making it a key technology for data science teams.

Essential Roles & Responsibilities of a Data Science With Python Course

  • Data Scientist: Data Scientist is responsible for analyzing large dataset and extracting valuable insight using Python tools like Pandas, NumPy, and Matplotlib. They design and implement machine learning model to predict future trends & solve complex business problems. Data scientists clean, process and prepare data for analysis, ensuring its quality and integrity They also communicate findings to stakeholders through visualizations and reports Their expertise is essential for helping organizations make data driven decisions.
  • Data Analyst: Data Analysts focus on collecting, processing, and performing on a data to help of organization make informed decision They use Python libraries like Pandas and Matplotlib to manipulate data and create visual report. Their role includes interpreting data trends and preparing dashboards for easier insights consumption. Data analysts ensure that data is accurate, complete and organized making it accessible for decision makers.
  • Machine Learning Engineer: The Machine Learning Engineer of develop an machine learning to solve business problems They leverage Python libraries like Scikit learn and TensorFlow to build and train algorithms They ensure that the models are scalable, efficient and integrated into the organization’s systems. Machine Learning Engineers also fine-tune algorithms for improved performance and maintain these models post-deployment. Their work enables predictive analytics and automates decision-making processes within organization.
  • Data Engineer: Data Engineer focus on building and maintaining architecture used for data generation, collection and storage. They use Python to work with big data tools like Apache Spark and Hadoop, ensuring that data flows efficiently through the system. They create pipelines that automate the extraction, transformation, and loading (ETL) of data Data Engineers also ensure that data is clean and accessible for data scientists and analysts. Their expertise supports the seamless integration of various data sources for analysis.
  • AI Specialist: AI Specialists are responsible for developing and deploying artificial intelligence models that solve real world problems They use Python libraries like Keras and TensorFlow Their role involves researching new AI techniques, applying them to specific use cases and refining algorithm for enhanced performance AI specialists work closely with machine learning engineers to ensure that AI models are scalable and efficient.
  • Business Intelligence Analyst: Business Intelligence Analysts use Python to analyze business data and provide actionable insights to improve business strategie They focu on using statistical method and data visualization to uncover patterns that influence business decision. Their role involves creating reports and dashboards using tool like Tableau or Python’s Plotly They also collaborate with different departments to understand their data need and tailor insight to their objective Their work is integral in helping companie optimize their operations and strategies.
  • Data Science Consultant: Data Science Consultants provide expert advice on how to utilize data science techniques for solving specific business problems. They analyze client data using Python and propose tailored solutions often including predictive modeling and machine learning Their role also includes recommending data collection methods, tools and technologies suited for the client’s needs They work closely with business stakeholders to define the scope & objective of project Consultants help organizations maximize the value of their data by offering expert insights and actionable strategies.
  • Statistical Analyst: Statistical Analysts apply statistical methods to analyze and interpret data using Python based tools such as SciPy and StatsModels. They design experiments and surveys, perform hypothesis testing, and identify trends within datasets Their responsibilities include ensuring the data used is statistically valid and can be generalized Statistical Analysts also interpret complex data and present findings to non technical stakeholders in a digestible format They help organizations make decisions based on statistical evidence and predictive modeling.
  • Data Science Instructor: Data Science Instructors are responsible for teaching students the principles of data science focusing on tools like Python machine learning & data visualization They design curricula, create learning materials and lead training sessions for aspiring data scientists. Instructors guide students through practical projects helping them develop hands on experience with real-world data. Their responsibilities also include staying updated with the latest industry trends and tools. They help shape the future generation of data professionals by equipping them with relevant skills and knowledge.
  • Data Visualization Expert: Data Visualization Experts specialize in creating compelling visual representation of data to make complex information easier to understand. They use Python libraries like Matplotlib, Seaborn, and Plotly to design charts, graphs and interactive dashboards. Their role involves working with data scientists and analyst to present findings in a visually appealing and understandable Data Visualization Experts ensure that the data is not only accurate but also easy for stakeholder to interpret.

Best Companies Seeking Data Science With Python Talent for Innovation

  • Google: Google is one of the leaders in the tech industry, heavily relying on data science to drive innovations like search algorithms, personalized recommendations, and AI research. The company seeks data scientists proficient in Python to build scalable data models and improve machine learning techniques. Google uses Python based frameworks like TensorFlow for deep learning and big data processing. Data scientist play a crucial role in handling vast amounts of data, ensuring precision in results Google encourages data driven decisions offering professional an environment to work on impactful large scale projects.
  • Amazon: Amazon has a massive reliance on data science are supply chain of customer recommendations. The company uses Python for predictive analytics, demand forecasting, and customer insights. Amazon’s data scientists develop algorithms to enhance logistics, inventory management, and user experience. Python professionals at Amazon work with cutting-edge machine learning and AI tools to solve business problems at scale. The company’s focus on automation and data-driven strategies offers endless opportunities for data science professionals.
  • Netflix: Netflix leverages data science to improve user recommendations, content optimization and to predict viewer preferences Python is used extensively for data analysis, predictive modeling, and personalization algorithms. Data scientists at Netflix develop machine learning models that help the platform deliver personalized viewing experiences. Python tools like Pandas, NumPy, and SciPy are integral to processing vast datasets and running complex analyses. Netflix offers an innovative environment where data science professionals play a key role in enhancing customer satisfaction.
  • Facebook (Meta): Meta (formerly Facebook) relies heavily on data science for developing social media algorithms, personalized content, and advertising Data scientists use Python for analyzing user behavior, content engagement and optimizing advertising strategies. Machine learning models built in Python help improve the accuracy of targeted ads and content recommendations. Meta’s data scientists collaborate with engineers to build scalable data systems that handle massive datasets The company offers dynamic opportunities for data professionals to influence billions of users worldwide.
  • IBM: IBM is an pioneer in AI and data science offering a wide range of solutions from cloud computing to cognitive analytics. Python plays a central role in the company's data-driven projects, with data scientists working on predictive analytics, AI and automation. IBM’s data science team uses Python for building models that help industries like healthcare, finance and retail optimize their operations. IBM fosters a culture of innovation, making it an ideal destination for data scientists.
  • Microsoft: Microsoft’s data science initiatives span product optimization, user behavior analysis, and AI development across its extensive product line, including Azure, Office 365 and Xbox. Data scientists at Microsoft use Python to build predictive models and enhance customer experiences. Python’s integration with Azure’s cloud services allows data scientists to scale and deploy machine learning models effectively. Microsoft’s research into AI and machine learning opens up opportunities for professionals to contribute to impactful technologies. Data science professionals at Microsoft work on diverse, global projects shaping the future of technology.
  • Apple: Apple uses data science for personalizing user experiences across its ecosystem, including iPhones, iPads and Macs. The company relies on Python for data analysis. Data scientists at Apple focus on machine learning models to improve user interaction, security, and product recommendations. Python is instrumental in analyzing large datasets and building real time applications that integrate seamlessly with Apple’s services Apples data science professionals contribute to the continuous evolution of one of the world’s most valuable tech ecosystems.
  • Uber: Uber utilizes data science and machine learning to optimize ride-sharing algorithms, pricing models and route prediction. Python is used extensively for building predictive models, data analysis and to enhance customer experience. Data scientists at Uber develop algorithms that handle complex logistics, improving efficiency and cost effectiveness in real time Pythons capabilities in machine learning and data processing enable Uber to make data driven decisions quickly The companys focus on scaling solutions offers significant opportunities for data professionals to work on innovative project.
  • Airbnb: Airbnb uses data science to refine its booking algorithms, predict property price and optimize search results Data scientists at Airbnb leverage Python to build models that enhance user experience and boost business growth Machine learning and statistical modeling in Python allow Airbnb to personalize listings and improve the platform’s overall functionality Data professionals at Airbnb work on global projects helping millions of users find ideal accommodations and making the booking process seamless The companys data driven culture makes it a top choice for Python professionals in the travel industry.
  • Spotify: Spotify relies on data science to curate personalized playlists, recommend new music and enhance user experience. Python is used in the backend for analyzing user behavior and refining the recommendation engine. Data scientists at Spotify develop machine learning models that analyze listening habits and predict future preferences. Python tools like TensorFlow and Keras help Spotify optimize algorithms making music discovery more intuitive The companys focus on innovation in music recommendation and data driven decision making offers exciting opportunities for Python trained data science professional.
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Data Science With Python Course Objectives

To ensure success in our Data Science With Python Training, participants should have a basic understanding of mathematics and statistics. Familiarity with programming concepts, especially in Python is advantageous but not mandatory as we cover the foundational concepts Participants with a strong analytical mindset and problem solving skills will find the course easier to grasp.
Our Data Science With Python Training equips students with essential skills needed in the fast-growing data science field. By mastering Python and its libraries like Pandas, NumPy and Matplotlib, participants will be able to analyze and visualize data effectively. The training emphasizes machine learning and artificial intelligence, two of the most in-demand skill sets in today's job market.
Data Science With Python Training is extremely vital in today’s job market, where data-driven decision-making is central to business strategies across industries. Python are being one of the most widely used on programming languages in data science, is in high demand by employers looking to leverage big data for insights and innovation.
  • Increased demand for AI and machine learning professionals
  • Expansion of data-driven decision-making across industries
  • Opportunities in emerging fields like robotics and autonomous systems
  • Growing use of big data analytics in healthcare, finance, and marketing
Yes our Data Science With Python Training includes real world projects that provide hands on experience with the concepts taught throughout the course Participants will work on live datasets and develop solutions for real world problem These projects focus on practical skills such as data wrangling, building machine learning models and visualizing data. By the end of the course students will have a portfolio of projects that demonstrate their capabilities making them job ready and confident in their skills.
  • Introduction to Python for Data Science
  • Data Wrangling and Preprocessing with Pandas
  • Data Visualization using Matplotlib and Seaborn
  • Introduction to Machine Learning with Scikit-learn
  • Supervised and Unsupervised Learning Techniques
Yes our Data Science With Python Training offers comprehensive placement support We have strong partnerships with leading companies in the tech and business sectors which helps in connecting students with job opportunities Our career support team provides resume-building workshops, interview preparation and personalized job search assistance. Students also gain access to our network of industry professionals, further increasing their chances of landing a job in the data science field. With over 500+ hiring partners, our placement support is a key part of your journey toward securing a job.
  • Healthcare
  • Finance
  • E-commerce
  • Marketing
  • Telecommunications
Data Science With Python Training in Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, and Jupyter Notebooks. These tools are widely used by data scientists and machine learning engineers for data manipulation, analysis, and model development expertise in using cloud platforms like AWS for deploying model. By the end of the course, participants will have the practical knowledge to handle a variety of data science tasks and implement machine learning solutions.
  • Access to high-paying job roles in data science
  • Comprehensive understanding of data manipulation, visualization and machine learning
  • Certification from a reputable institution recognized by top employer
  • Increased job market visibility and opportunities for career advancement
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Data Science With Python Course Benefits

The Data Science With Python Certification Course equip learners with essential skill in data analysis, machine learning and data visualization using Python. By mastering tools like Pandas, NumPy and TensorFlow, participant gain hands on experience in solving real world problem. This training opens doors to lucrative career opportunities, boosts employability and provides a strong foundation for advanced data science roles.

  • Designation
  • Annual Salary
    Hiring Companies
  • 4.24L
    Min
  • 6.5L
    Average
  • 12.5L
    Max
  • 3.50L
    Min
  • 7.5L
    Average
  • 13.5L
    Max
  • 4.0L
    Min
  • 6.50L
    Average
  • 14.0L
    Max
  • 4.34L
    Min
  • 8.5L
    Average
  • 15.5L
    Max

About Your Data Science With Python Certification Training

Our Data Science With Python Online Training provides an affordable, comprehensive learning path covering Python fundamentals, data manipulation, machine learning and visualization With 500+ hiring partners we offer excellent career opportunities and 100% placement support ensuring you develop practical skills to thrive in the competitive data science industry.

Top Skills You Will Gain
  • Data Manipulation
  • Machine Learning
  • Data Visualization
  • Statistical Analysis
  • Data Cleaning
  • Predictive Modeling
  • Deep Learning
  • Feature Engineering
  • Python Programming

12+ Data Science With Python Tools

Online Classroom Batches Preferred

Weekdays (Mon - Fri)
14-July-2025
08:00 AM (IST)
Weekdays (Mon - Fri)
16-July-2025
08:00 AM (IST)
Weekend (Sat)
19-July-2025
11:00 AM (IST)
Weekend (Sun)
20-July-2025
11:00 AM (IST)
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Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

Why Data Science With Python Course From Learnovita ? 100% Money Back Guarantee

Data Science With Python Course Curriculum

Trainers Profile

Our Data Science With Python Online Training instructors are committed to delivering the latest curriculum, incorporating industry leading techniques and insights With globally recognized certifications our trainers bring a wealth of experience to ensure students receive up to date knowledge and practical skills Through their guidance participants gain expertise that aligns with the fast-evolving demands of the job market Additionally our Data Science With Python Internship program provides hands on experience enhancing your learning journey and career prospects.

Syllabus for Data Science With Python Course Download syllabus

  • Python Basics
  • Data Types
  • Control Structures
  • Functions & Modules
  • Libraries Installation
  • Pandas Introduction
  • DataFrames Operations
  • Data Indexing
  • Merging DataFrames
  • Missing Data
  • Matplotlib Basics
  • Plotting Graphs
  • Customizing Plots
  • Seaborn Introduction
  • Advanced Visualizations
  • Array Indexing
  • Mathematical Functions
  • Linear Algebra
  • Random Sampling
  • Statistical Operations
  • Handling Missing Values
  • Data Normalization
  • Feature Scaling
  • Encoding Categorical Data
  • Removing Duplicates
  • Probability Distributions
  • Hypothesis Testing
  • Correlation & Covariance
  • Sampling Techniques
  • Confidence Intervals
  • Supervised Learning
  • Unsupervised Learning
  • Model Evaluation
  • Cross-Validation
  • Model Training
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forest
  • TensorFlow Basics
  • Neural Networks
  • Feedforward Networks
  • Backpropagation
  • Activation Functions
  • Text Preprocessing
  • Tokenization
  • Word Embeddings
  • TF-IDF
  • Sentiment Analysis
(15) view More view Less
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Industry Projects

Project 1
Predictive Sales Analysis

In this project, participants will build a machine learning model to predict sales trends based on historical data. Using Python libraries like Pandas, NumPy and Scikit-learn they will preprocess the data, engineer features, and apply regression models.

Project 2
Customer with K-Means Clustering

Participants will use unsupervised learning techniques to segment customers based on purchase behavior and demographic data. Using Pythons Scikit learn and Seaborn they will apply K Means clustering to identify distinct customer groups providing valuable insights.

Project 3
Sentiment Analysis on Social Media Data

In this project, participants will apply natural language processing (NLP) data and determine the sentiment of user posts. Python tools like NLTK, SpaCy, and TextBlob will be used for text preprocessing, tokenization and sentiment classification.

Career Support

Our Hiring Partner

Exam & Data Science With Python Certification

  • Basic understanding of programming concepts
  • Familiarity with mathematical and statistical principles
  • Knowledge of Python programming
  • Willingness to learn and engage with data science concepts
  • Strong analytical thinking and problem-solving skills
Obtaining a Data Science With Python Certification provides several key advantage It serves as a formal recognition of your expertise in data science and Python programming making you more attractive to potential employers It enhances your credibility as a professional capable of handling complex data analysis tasks, predictive modeling, and machine learning projects.
While the Data Science With Python Certification significantly boosts your job prospects it does not guarantee employment on its own It provides you with the necessary skills and a strong portfolio of projects to demonstrate your expertise to employers. However securing a job will also depend on factors such as your practical experience interview performance and ability to showcase problem solving skills. Our placement support team helps guide you through the job search process, ensuring you are well prepared to land a role.
The Data Science With Python Certification opens up numerous opportunities for career growth by equipping you with the essential skills that are in high demand across various industries It boosts your credibility & marketability making you a more attractive candidate to employers seeking expertise in data science and machine learning. With hands on training in Python, data analysis and machine learning you can take on challenging roles and projects This certification can lead to higher paying job opportunities, promotions and career progression setting you on a path toward long term success in data science.
  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Business Intelligence Analyst
  • AI Engineer

Our Student Successful Story

checkimage Regular 1:1 Mentorship From Industry Experts checkimage Live Classes checkimage Career Support

How are the Data Science with Python Course with LearnoVita Different?

Feature

LearnoVita

Other Institutes

Affordable Fees

Competitive Pricing With Flexible Payment Options.

Higher Data Science with Python Fees With Limited Payment Options.

Live Class From ( Industry Expert)

Well Experienced Trainer From a Relevant Field With Practical Data Science with Python Training

Theoretical Class With Limited Practical

Updated Syllabus

Updated and Industry-relevant Data Science with Python Course Curriculum With Hands-on Learning.

Outdated Curriculum With Limited Practical Training.

Hands-on projects

Real-world Data Science with Python Projects With Live Case Studies and Collaboration With Companies.

Basic Projects With Limited Real-world Application.

Certification

Industry-recognized Data Science with Python Certifications With Global Validity.

Basic Data Science with Python Certifications With Limited Recognition.

Placement Support

Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

Basic Placement Support

Industry Partnerships

Strong Ties With Top Tech Companies for Internships and Placements

No Partnerships, Limited Opportunities

Batch Size

Small Batch Sizes for Personalized Attention.

Large Batch Sizes With Limited Individual Focus.

Additional Features

Lifetime Access to Data Science with Python Course Materials, Alumni Network, and Hackathons.

No Additional Features or Perks.

Training Support

Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.

Limited Mentor Support and No After-hours Assistance.

Data Science With Python Course FAQ's

Certainly, you are welcome to join the demo session. However, due to our commitment to maintaining high-quality standards, we limit the number of participants in live sessions. Therefore, participation in a live class without enrollment is not feasible. If you're unable to attend, you can review our pre-recorded session featuring the same trainer. This will provide you with a comprehensive understanding of our class structure, instructor quality, and level of interaction.
All of our instructors are employed professionals in the industry who work for prestigious companies and have a minimum of 9 to 12 years of significant IT field experience. A great learning experience is provided by all of these knowledgeable people at LearnoVita.
  • LearnoVita is dedicated to assisting job seekers in seeking, connecting, and achieving success, while also ensuring employers are delighted with the ideal candidates.
  • Upon successful completion of a career course with LearnoVita, you may qualify for job placement assistance. We offer 100% placement assistance and maintain strong relationships with over 650 top MNCs.
  • Our Placement Cell aids students in securing interviews with major companies such as Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, Sports One , Infosys, MindTree, and MPhasis, among others.
  • LearnoVita has a legendary reputation for placing students, as evidenced by our Placed Students' List on our website. Last year alone, over 5400 students were placed in India and globally.
  • We conduct development sessions, including mock interviews and presentation skills training, to prepare students for challenging interview situations with confidence. With an 85% placement record, our Placement Cell continues to support you until you secure a position with a better MNC.
  • Please visit your student's portal for free access to job openings, study materials, videos, recorded sections, and top MNC interview questions.
LearnoVita Certification is awarded upon course completion and is recognized by all of the world's leading global corporations. LearnoVita are the exclusive authorized Oracle, Microsoft, Pearson Vue, and Data Science with Python I exam centers, as well as an authorized partner of Data Science with Python . Additionally, those who want to pass the National Authorized Certificate in a specialized IT domain can get assistance from LearnoVita's technical experts.
As part of the training program, LearnoVita provides you with the most recent, pertinent, and valuable real-world projects. Every program includes several projects that rigorously assess your knowledge, abilities, and real-world experience to ensure you are fully prepared for the workforce. Your abilities will be equivalent to six months of demanding industry experience once the tasks are completed.
At LearnoVita, participants can choose from instructor-led online training, self-paced training, classroom sessions, one-to-one training, fast-track programs, customized training, and online training options. Each mode is designed to provide flexibility and convenience to learners, allowing them to select the format that best suits their needs. With a range of training options available, participants can select the mode that aligns with their learning style, schedule, and career goals to excel in Data Science with Python .
LearnoVita guarantees that you won't miss any topics or modules. You have three options to catch up: we'll reschedule classes to suit your schedule within the course duration, provide access to online class presentations and recordings, or allow you to attend the missed session in another live batch.
Please don't hesitate to reach out to us at contact@learnovita.com if you have any questions or need further clarification.
To enroll in the Data Science with Python at LearnoVita, you can conveniently register through our website or visit any of our branches in India for direct assistance.
Yes, after you've enrolled, you will have lifetime access to the student portal's study materials, videos, and top MNC interview questions.
At LearnoVita, we prioritize individual attention for students, ensuring they can clarify doubts on complex topics and gain a richer understanding through interactions with instructors and peers. To facilitate this, we limit the size of each Data Science with Python Service batch to 5 or 6 members.
The average annual salary for Data Science with Python Professionals in India is 5 LPA to 8 LPA.
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